A Hybrid Approach to Query and Document Translation Using a Pivot Language for Cross-Language Information Retrieval

Kishida, Kazuaki; Kando, Noriko

doi:10.1007/11878773_10

Cited by 12 publications

(7 citation statements)

References 4 publications

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“…These results, however, depended on the specifics of the translation technique, the language pair, as well as the quality of the underlying translation system. With different setups, later papers contradicted earlier conclusions, instead claiming that query translation outperforms document translation [Hayurani et al 2007], and that there is no benefit from translating in both directions [Kishida and Kando 2006].…”

Section: Document Translation and Language-independent Approachesmentioning

confidence: 71%

Exploiting Representations from Statistical Machine Translation for Cross-Language Information Retrieval

Ture

Lin

2014

ACM Trans. Inf. Syst.

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This work explores how internal representations of modern statistical machine translation systems can be exploited for cross-language information retrieval. We tackle two core issues that are central to query translation: how to exploit context to generate more accurate translations and how to preserve ambiguity that may be present in the original query, thereby retaining a diverse set of translation alternatives. These two considerations are often in tension since ambiguity in natural language is typically resolved by exploiting context, but effective retrieval requires striking the right balance. We propose two novel query translation approaches: the grammar-based approach extracts translation probabilities from translation grammars, while the decoder-based approach takes advantage of n-best translation hypotheses. Both are context-sensitive, in contrast to a baseline context-insensitive approach that uses bilingual dictionaries for word-by-word translation. Experimental results show that by "opening up" modern statistical machine translation systems, we can access intermediate representations that yield high retrieval effectiveness. By combining evidence from multiple sources, we demonstrate significant improvements over competitive baselines on standard crosslanguage information retrieval test collections. In addition to effectiveness, the efficiency of our techniques are explored as well. ACM Reference Format:Ferhan Ture and Jimmy Lin. 2014. Exploiting representations from statistical machine translation for crosslanguage information retrieval.

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Section: Document Translation and Language-independent Approachesmentioning

confidence: 71%

Exploiting Representations from Statistical Machine Translation for Cross-Language Information Retrieval

Ture

Lin

2014

ACM Trans. Inf. Syst.

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“…The literature shows that DT-CLIA and QT-CLIA have their own strengths and weaknesses, so there are studies on combining DT and QT in CLIA (Chen and Gey, 2003;Kishida and Kando, 2006), but the documents in those studies were often translated by machine readable dictionaries rather than MT.…”

Section: Related Workmentioning

confidence: 99%

“…However, in QT-CLIA, it is the queries that are translated, and the retrieval performance can suffer because of the translation ambiguities introduced in query translation. Therefore, there have been studies on combining QT and DT in CLIA (Kishida and Kando, 2006). In fact, using information from multiple sources to improve the performance is a common practice in many studies.…”

Section: Mt For Oov Termsmentioning

confidence: 99%

Exploring the further integration of machine translation in English‐Chinese cross language information access

He²

2012

Program

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Purpose -This paper seeks to examine the further integration of machine translation technologies with cross language information access in providing web users the capabilities of accessing information beyond language barriers. Machine translation and cross language information access are related technologies, and yet they have their own unique contributions in handling information in multiple languages. This paper aims to demonstrate that there are many opportunities to further integrate machine translation with cross language information access, and the combination can greatly empower web users in their information access. Design/methodology/approach -Using English and Chinese as the language pair for studying, this paper looks at machine translation in query translation-based cross language information access at multiple important aspects, which include query translation, relevance feedback, interactive cross language information access, out-of-vocabulary term translation, and data fusion. The goal is to obtain more insights about the wide range usages of machine translation in cross language information access, and to help the community to identify promising future directions for both machine translation and cross language access. Findings -Machine translation can be applied effectively in many places in the whole cross language information access process. Queries translated by a machine translation system are high quality and are more robust in handling potential untranslated terms. Translation enhancement, a relevance feedback method using machine translation generated returned documents, is not only a valid technique by itself, but also helps to generate more robust cross language information access performance when combined with other relevance feedback techniques. Machine translation is also found to play a significant role in resolving untranslated terms and in data fusion. Originality/value -This set of comparative empirical studies on integrating machine translation and cross language information access was performed on a common evaluation framework, and examined integration at multiple points of the cross language access process. The experimental results demonstrate the value of further integrating machine translation in cross language information access, and identify interesting future directions for both machine translation and cross language information access research.

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“…Particularly, when the document collection is large and search is to be carried out across multiple language pairs. A less common CLIR technique which has shown to be effective, is the Hybrid CLIR approach which utilises both document and query translation approaches, thus allowing the relative advantages of both approaches to complement each other (McCarley, 1999;Kishida & Kando, 2006;Parton, McKeown, Allan, & Henestroza, 2008).…”

Section: Cross Language Information Retrievalmentioning

confidence: 99%

Utilisation of Metadata Fields and Query Expansion in Cross-Lingual Search of User-Generated Internet Video

Ahmad¹,

Ganguly²,

Jones³

2016

jair

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Recent years have seen significant efforts in the area of Cross Language Information Retrieval (CLIR) for text retrieval. This work initially focused on formally published content, but more recently research has begun to concentrate on CLIR for informal social media content. However, despite the current expansion in online multimedia archives, there has been little work on CLIR for this content. While there has been some limited work on Cross-Language Video Retrieval (CLVR) for professional videos, such as documentaries or TV news broadcasts, there has to date, been no significant investigation of CLVR for the rapidly growing archives of informal user generated (UGC) content. Key differences between such UGC and professionally produced content are the nature and structure of the textual UGC metadata associated with it, as well as the form and quality of the content itself. In this setting, retrieval effectiveness may not only suffer from translation errors common to all CLIR tasks, but also recognition errors associated with the automatic speech recognition (ASR) systems used to transcribe the spoken content of the video and with the informality and inconsistency of the associated user-created metadata for each video. This work proposes and evaluates techniques to improve CLIR effectiveness of such noisy UGC content. Our experimental investigation shows that different sources of evidence, e.g. the content from different fields of the structured metadata, significantly affect CLIR effectiveness. Results from our experiments also show that each metadata field has a varying robustness to query expansion (QE) and hence can have a negative impact on the CLIR effectiveness. Our work proposes a novel adaptive QE technique that predicts the most reliable source for expansion and shows how this technique can be effective for improving CLIR effectiveness for UGC content.

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A Hybrid Approach to Query and Document Translation Using a Pivot Language for Cross-Language Information Retrieval

Cited by 12 publications

References 4 publications

Exploiting Representations from Statistical Machine Translation for Cross-Language Information Retrieval

Exploiting Representations from Statistical Machine Translation for Cross-Language Information Retrieval

Exploring the further integration of machine translation in English‐Chinese cross language information access

Utilisation of Metadata Fields and Query Expansion in Cross-Lingual Search of User-Generated Internet Video

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